Effect of non-Gaussian turbulence on extreme buffeting response of a high-speed railway sea-crossing bridge

Xu, Zhongying; Griffiths, D. Ll.; Zhang, Limao; Chen, Y. Frank; Flay, Richard G.J.; Rao, Huiming

doi:10.1016/j.jweia.2022.104981

Cited by 13 publications

(1 citation statement)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we focus on the bispectral analysis, which consists in analyzing how the third statistical moment of the input in a dynamical system is filtered by the system. In structural engineering, this type of analysis is useful when the loads applied on structures are non Gaussian (hence represented by their successive statistical moments, of order higher than 2), as it is also the case in wind engineering [17,18,19,20,21] or marine engineering [22,23]. Similarly to the spectral analysis which requires integration of the PSD to obtain a variance, in the bispectral analysis, the third statistical moment of any quantity is obtained by the integral of the corresponding bispectrum.…”

Section: Introductionmentioning

confidence: 99%

A multiple timescale approach of bispectral correlation

Denoël

Marzino

Geuzaine

2023

Journal of Wind Engineering and Industrial Aerodynamics

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A multiple timescale approach of bispectral correlation

Denoël

Marzino

Geuzaine

2023

Journal of Wind Engineering and Industrial Aerodynamics

View full text Add to dashboard Cite

Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

Chhotu,

Suman

2024

Urban Rail Transit

View full text Add to dashboard Cite

With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods.

show abstract